Multi-Access Edge Computing (MEC) is a promising paradigm that brings computational capabilities closer to end-users, enabling low-latency and high-bandwidth applications. The federation of multiple MEC platforms has the potential to create a collaborative ecosystem, facilitating resource sharing and scalability. This paper addresses the benefits of exploring the synergies between Software-Defined Networking (SDN) and MEC federation deployments, aiming to enhance the network infrastructure’s overall performance, flexibility, and responsiveness. More concretely, this work explores the integration of MEC and SDN to address performance and resource utilization challenges in congested federated MEC environments, enabling seamless service migration between MEC nodes when resource constraints arise with results showing its ability to maintain service quality under congestion.

This paper investigates the development of a solution for Edge Internet Traffic Steering (EITS), focusing on optimizing the performance of services at the network edge using Computing-Aware Traffic Steering (CATS) principles. Motivated by the increasing demand for low-latency connectivity, high bandwidth, resource availability, link redundancy, and efficient memory and CPU usage optimization, this research addresses the limitations of existing traffic steering approaches in meeting the static or dynamic requirements of edge services. The proposed solution leverages network and compute metrics to direct traffic to the most suitable service instance, enhancing overall performance through the use of OpenAirInterface 5G Core Network. The practical implementation of a video streaming use case demonstrated the efficacy of the solution.

Edge computing brings added benefits for different elements in the overall system (e.g., users, operators and service providers). However, currently there are no proper interfaces and mechanisms to instantiate third-party services within the operators' infrastructure (e.g., as a MEC application), thus hindering edge computing to reach its full potential. To fill this gap, this paper presents architectural enhancements, interfaces and mechanisms to enable dynamic and efficient third-party service deployment within the operators' domain. A simulation-based analysis is presented to showcase the relevance of the proposed solution. Results highlighted the benefits of optimal migration of third-party services into a distributed setting, compared to the unveiled drawbacks of a centralized approach. In addition, the key components of the solution are implemented and experimentally validated through a proof-of-concept prototype showcasing the performance impact of the proposed approach as well as the suitability of its implementation.

Multiaccess edge computing (MEC) service migration is a technology whose key objective is to support ultralow-latency access to services. However, the complex ultralarge-scale edge service migration problem requires extensive research efforts, regarding the foreseen ultradensified edge nodes in 5G and beyond. In this article, we propose a novel dynamic service migration optimization architecture for ultralarge-scale MEC networks. We develop a new multicriteria decision-making algorithm: Technique for order of preference by similarity to ideal solution with attribute-based Niche count, named TOPANSIS, which showcases its strength to provide an optimal solution for service migration in large-scale deployments towards optimal data rate, latency, and load balancing. We further decentralize the operation of TOPANSIS to release the traffic burden from central datacenters by leveraging local decision making by edge nodes, while relying on central cloud coordination to account for the overall network information. Simulation results showcase that the proposed architecture outperforms the selected benchmarks with an average improvement of 39.41% for latency, 2.92% for data rate, as well as 10.53% and 6.26% for RAM and CPU load balancing, respectively. Moreover, the feasibility of the proposed solution is validated by means of a proof-of-concept implementation and experimental assessments.

In this paper, we modeled the emerging edge-computing-enabled live service migration as a multi-criteria problem optimization, tackling migration costs and benefits, as well as discussion of service providers' data privacy, simultaneously. Based on the optimization formulation, we conducted a small-scale analytical feasibility test, considering widely-utilized multi-criteria decision making algorithms, based on which we proposed a new TOPSIS based service migration algorithm. The algorithm was evaluated using simulations, whose results show that the proposed algorithm is sufficient to support live service migration for heterogeneous edge computing, while outperforming benchmarks in with respect to reducing migration costs and increasing achieved benefits, by 34.52% and 60.21%, respectively.

This paper presents a framework for improved mobile video delivery in high speed vehicles, integrating Network Function Virtualization, Software Defined Networking and Multi-access Edge Computing (MEC), with user mobility and service consumption context for enabling the dynamic instantiation of video services in edge-positioned virtualized Content Delivery Network (vCDN) nodes. Based on the estimated vehicle (current and future) position, the solution determines: where the video should be dynamically cached and at what time; and which chunks of the video need to be cached at the target location. This "location-aware predictive caching" mechanism is able to operate in a transparent way to both the user and the video service, by handling the necessary flow-based mobility procedures. A proof of concept was implemented and compared with a static caching service and a regular MEC-based vCDN, enabling the assessment of the contributions and impacts of the mechanism. Results show that our solution minimized the number of cache misses in mobility scenarios while reducing backhaul and core network traffic.

The foreseen exploding number of devices connected to the mobile network has been pushing operators to look for mechanisms to transparently offload their data. However, such procedures need to consider the different demands from users' traffic and involved access networks. Towards that end, Network Slicing has been proposing a logical partition of the network to better serve different verticals requirements, while providing isolation, achieved by using Software Defined Networking (SDN) and Network Function Virtualisation (NFV) mechanisms. In this paper, we explore the realization of a mobile traffic offloading framework that is able to dynamically instantiate a non-3GPP slice and use SDN mechanisms to seamlessly handover existing flows into the new connection point. For this, we virtualized both the mobile Core Network components as well as the User Equipment, allowing the mobility procedure to be handled in the cloud and become transparent to the physical endpoints. A prototype of our proposal was deployed in a physical testbed, and evaluated in a mobile video offloading scenario where the operator strategically deploys Wi-Fi access points around the city, with results showing that the user is able to continuously visualize the video while switching access technology.

Because of the diversity of portfolios based on assets throughout international markets, exchange rate prediction plays an important role in risk management, asset allocation, and trading strategies. This paper aims to investigate the use of a recent paradigm of recurrent neural networks, echo state networks (ESNs), applied to forecasting and trading currency exchange rates. It does so by benchmarking the statistical and trading performance of ESNs against a naïve strategy, an Autoregressive Moving Average (ARMA) model, and a multilayer perceptron neural network. One can interpret ESNs as a recurrent structure that provides both the simplicity of the resulting mathematical model and the ability to express a wide range of nonlinear and time-varying dynamics. As an application, this paper carries out computational experiments that include the Brazilian Real, the European Union Euro, the Japanese Yen, and British Pounds with American Dollar exchange rates from January 4, 2000, through December 31, 2012. The results reveal that the ESN and the ARMA model provide similar results, statistically outperforming the other models in terms of accuracy. However, when trading indicators are considered, the performance of the ESN is superior to that of the alternative approaches.